Systems and methods for configuring and using a multi-stage object classification and condition pipeline

ABSTRACT

A system, method, and computer-program product includes detecting, via a localization machine learning model, a target object within a scene based on downsampled image data of the scene, identifying a likely position of the target object within original image data of the scene, extracting, from the original image data of the scene, a target sub-image containing the target object, classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes, routing the target image resolution of the target sub-image to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models, classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class, and displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/431,277, filed on 8 Dec. 2022, which is incorporated in its entiretyby this reference.

TECHNICAL FIELD

This invention relates generally to the machine learning field, and morespecifically, to new and useful systems and methods for configuring andusing a multi-stage object classification and condition pipeline.

BACKGROUND

Traditionally, infrastructure maintenance and reliability assessmentsare performed by human operators. These human operators are tasked withthe responsibility of visually inspecting and assessing infrastructurecomponents. This process can be time-consuming, labor-intensive, andrequires a high level of specialized knowledge and training in thespecific field of the infrastructure components being assessed.Furthermore, this process fails to scale effectively in instances wherehundreds or thousands of infrastructure components require assessment.

Therefore, there is a need in the art for using machine learning toaccelerate the process of infrastructure maintenance and reliabilityassessments. The embodiments of the present application providetechnical solutions that address, at least, the needs described above,as well as the deficiencies of the start of the art.

BRIEF SUMMARY OF THE INVENTION(S)

In one embodiment, a computer-program product embodied in anon-transitory machine-readable storage medium storing computerinstructions that, when executed by one or more processors, performoperations including detecting, via a localization machine learningmodel, a target object within a scene based on downsampled image data ofthe scene; identifying, via the one or more processors, a likelyposition of the target object within original image data of the scenebased on object position data computed by the localization machinelearning model; extracting, from the original image data of the scene, atarget sub-image containing the target object based on the likelyposition of the target object; classifying, via an object classificationmachine learning model, the target object to a probable object class ofa plurality of distinct object classes based on a target imageresolution of the target sub-image, wherein the plurality of distinctobject classes includes an out-of-scope object class and one or morein-scope object classes; routing, via the one or more processors, thetarget image resolution of the target sub-image to a targetobject-condition machine learning classification model of a plurality ofdistinct object-condition machine learning classification models basedon a mapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels; classifying, via the target object-condition machine learningclassification model, the target object to a probable object-conditionclass of a plurality of distinct object-condition classes; anddisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition classbased on the classifying of the target object via the targetobject-condition machine learning classification model.

In one embodiment, the computer-program product further includesgenerating the localization machine learning model, wherein generatingthe localization machine learning model includes implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.

In one embodiment, the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.

In one embodiment, the object position data computed by the localizationmachine learning model defines an approximate spatial area of the targetobject in the downsampled image data of the scene; identifying thelikely position of the target object within the original image data ofthe scene includes correlating, via the one or more processors, theapproximate spatial area of the target object to a distinct spatial areain the original image data of the scene; and the target sub-imageextracted from the original image data of the scene includes thedistinct spatial area.

In one embodiment, the scene further includes a second object distinctfrom the target object; the computer-program product further includescomputer instructions for performing operations including identifying,via the one or more processors, a likely position of the second objectwithin the original image data of the scene based on the object positiondata computed by the localization machine learning model; andextracting, from the original image data of the scene, a secondsub-image containing the second object based on the likely position ofthe second object.

In one embodiment, the computer-program product further includesclassifying, via the object classification machine learning model, thesecond object to a probable object class of the plurality of distinctobject classes based on a target image resolution of the secondsub-image, wherein: the object classification machine learning modelclassifies the second object to the out-of-scope object class and thetarget object to one of the one or more in-scope object classes; theout-of-scope object class indicates that the second sub-image isimmaterial to a target classification objective of the plurality ofdistinct object-condition machine learning classification models; andthe one of the one or more in-scope object classes indicates that thetarget sub-image is germane to the target classification objective ofthe plurality of distinct object-condition machine learningclassification models.

In one embodiment, the computer-program product further includesbypassing an object-condition classification of the second sub-imagewhen the object classification machine learning model classifies thesecond object to the out-of-scope object class.

In one embodiment, the computer-program further includes classifying,via the object classification machine learning model, the second objectto a probable object class of the plurality of distinct object classesbased on a target image resolution of the second sub-image, wherein: theone or more in-scope object classes include a first in-scope objectclass and a second in-scope object class; the object classificationmachine learning model classifies the target object to the firstin-scope object class; the object classification machine learning modelclassifies the second object to the second in-scope object class; andwherein the routing further includes: routing the target imageresolution of the target sub-image to a distinct object-conditionmachine learning classification model of the plurality of distinctobject-condition machine learning classification models that correspondsto the first in-scope object class; and routing the target imageresolution of the second sub-image to a distinct object-conditionmachine learning classification model of the plurality of distinctobject-condition machine learning classification models that correspondsto the second in-scope object class.

In one embodiment, the mapping between the plurality of distinct objectclasses and the plurality of distinct object-condition machine learningclassification models defines a plurality of distinct routing nexuses;each distinct routing nexus of the plurality of distinct routing nexusesassociates a distinct one of the plurality of distinct object classes toa distinct one of the plurality of distinct object-condition machinelearning classification models; and the routing of the target imageresolution of the target sub-image is further based on identifying atarget routing nexus that corresponds to the probable object class ofthe target object.

In one embodiment, the probable object class indicates a probable objecttype of the target object; and the probable object-condition classindicates a probable operating state of the target object.

In one embodiment, the computer-program further includes implementing amulti-stage object classification and condition pipeline, wherein: afirst stage of the multi-stage object classification and conditionpipeline includes the detecting, by the localization machine learningmodel, of the target object within the scene; a second stage of themulti-stage object classification and condition pipeline includes theclassifying, by the object classification machine learning model, of thetarget object to the probable object class; and a third stage of themulti-stage object classification and condition pipeline includes theclassifying, by the target object-condition machine learningclassification model, of the target object to the probableobject-condition class.

In one embodiment, the original image data of the scene has a firstimage resolution; and the downsampled image data of the scene has asecond image resolution lower than the first image resolution.

In one embodiment, the downsampled image data of the scene satisfies atarget image resolution parameter of the localization machine learningmodel.

In one embodiment, a computer-implemented method includes detecting, viaa localization machine learning model, a target object within a scenebased on downsampled image data of the scene; identifying, via the oneor more processors, a likely position of the target object withinoriginal image data of the scene based on object position data computedby the localization machine learning model; extracting, from theoriginal image data of the scene, a target sub-image containing thetarget object based on the likely position of the target object;classifying, via an object classification machine learning model, thetarget object to a probable object class of a plurality of distinctobject classes based on a target image resolution of the targetsub-image, wherein the plurality of distinct object classes includes anout-of-scope object class and one or more in-scope object classes;routing, via the one or more processors, the target image resolution ofthe target sub-image to a target object-condition machine learningclassification model of a plurality of distinct object-condition machinelearning classification models based on a mapping between the pluralityof distinct object classes and the plurality of distinctobject-condition machine learning classification models; classifying,via the target object-condition machine learning classification model,the target object to a probable object-condition class of a plurality ofdistinct object-condition classes; and displaying, via a graphical userinterface, a representation of the target object in association with theprobable object-condition class based on the classifying of the targetobject via the target object-condition machine learning classificationmodel.

In one embodiment, the computer-implemented further includes generatingthe localization machine learning model, wherein generating thelocalization machine learning model includes: implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.

In one embodiment, the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.

In one embodiment, the object position data computed by the localizationmachine learning model defines an approximate spatial area of the targetobject in the downsampled image data of the scene; identifying thelikely position of the target object within the original image data ofthe scene includes: correlating, via the one or more processors, theapproximate spatial area of the target object to a distinct spatial areain the original image data of the scene; and the target sub-imageextracted from the original image data of the scene includes thedistinct spatial area.

In one embodiment, the scene further includes a second object distinctfrom the target object; the computer-program product further includescomputer instructions for performing operations including: identifying,via the one or more processors, a likely position of the second objectwithin the original image data of the scene based on the object positiondata computed by the localization machine learning model; andextracting, from the original image data of the scene, a secondsub-image containing the second object based on the likely position ofthe second object.

In one embodiment, the computer-implemented further includesclassifying, via the object classification machine learning model, thesecond object to a probable object class of the plurality of distinctobject classes based on a target image resolution of the secondsub-image, wherein: the object classification machine learning modelclassifies the second object to the out-of-scope object class and thetarget object to one of the one or more in-scope object classes; theout-of-scope object class indicates that the second sub-image isimmaterial to a target classification objective of the plurality ofdistinct object-condition machine learning classification models; andthe one of the one or more in-scope object classes indicates that thetarget sub-image is germane to the target classification objective ofthe plurality of distinct object-condition machine learningclassification models.

In one embodiment, the computer-implemented further includes bypassingan object-condition classification of the second sub-image when theobject classification machine learning model classifies the secondobject to the out-of-scope object class.

In one embodiment, the computer-implemented further includesclassifying, via the object classification machine learning model, thesecond object to a probable object class of the plurality of distinctobject classes based on a target image resolution of the secondsub-image, wherein: the one or more in-scope object classes include afirst in-scope object class and a second in-scope object class; theobject classification machine learning model classifies the targetobject to the first in-scope object class; the object classificationmachine learning model classifies the second object to the secondin-scope object class; and wherein the routing further includes: routingthe target image resolution of the target sub-image to a distinctobject-condition machine learning classification model of the pluralityof distinct object-condition machine learning classification models thatcorresponds to the first in-scope object class; and routing the targetimage resolution of the second sub-image to a distinct object-conditionmachine learning classification model of the plurality of distinctobject-condition machine learning classification models that correspondsto the second in-scope object class.

In one embodiment, the mapping between the plurality of distinct objectclasses and the plurality of distinct object-condition machine learningclassification models defines a plurality of distinct routing nexuses;each distinct routing nexus of the plurality of distinct routing nexusesassociates a distinct one of the plurality of distinct object classes toa distinct one of the plurality of distinct object-condition machinelearning classification models; and the routing of the target imageresolution of the target sub-image is further based on identifying atarget routing nexus that corresponds to the probable object class ofthe target object.

In one embodiment, the probable object class indicates a probable objecttype of the target object; and the probable object-condition classindicates a probable operating state of the target object.

In one embodiment, the computer-implemented method further includesimplementing a multi-stage object classification and condition pipeline,wherein: a first stage of the multi-stage object classification andcondition pipeline includes the detecting, by the localization machinelearning model, of the target object within the scene; a second stage ofthe multi-stage object classification and condition pipeline includesthe classifying, by the object classification machine learning model, ofthe target object to the probable object class; and a third stage of themulti-stage object classification and condition pipeline includes theclassifying, by the target object-condition machine learningclassification model, of the target object to the probableobject-condition class.

In one embodiment, the original image data of the scene has a firstimage resolution; and the downsampled image data of the scene has asecond image resolution lower than the first image resolution.

In one embodiment, the downsampled image data of the scene satisfies atarget image resolution parameter of the localization machine learningmodel.

In one embodiment, a computer-implemented system includes one or moreprocessors; a memory; a computer-readable medium operably coupled to theone or more processors, the computer-readable medium havingcomputer-readable instructions stored thereon that, when executed by theone or more processors, cause a computing device to perform operationscomprising: detecting, via a localization machine learning model, atarget object within a scene based on downsampled image data of thescene; identifying, via the one or more processors, a likely position ofthe target object within original image data of the scene based onobject position data computed by the localization machine learningmodel; extracting, from the original image data of the scene, a targetsub-image containing the target object based on the likely position ofthe target object; classifying, via an object classification machinelearning model, the target object to a probable object class of aplurality of distinct object classes based on a target image resolutionof the target sub-image, wherein the plurality of distinct objectclasses includes an out-of-scope object class and one or more in-scopeobject classes; routing, via the one or more processors, the targetimage resolution of the target sub-image to a target object-conditionmachine learning classification model of a plurality of distinctobject-condition machine learning classification models based on amapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels; classifying, via the target object-condition machine learningclassification model, the target object to a probable object-conditionclass of a plurality of distinct object-condition classes; anddisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition classbased on the classifying of the target object via the targetobject-condition machine learning classification model.

In one embodiment, the computer-implemented system further includesgenerating the localization machine learning model, wherein generatingthe localization machine learning model includes: implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.

In one embodiment, the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.

In one embodiment, the object position data computed by the localizationmachine learning model defines an approximate spatial area of the targetobject in the downsampled image data of the scene; identifying thelikely position of the target object within the original image data ofthe scene includes: correlating, via the one or more processors, theapproximate spatial area of the target object to a distinct spatial areain the original image data of the scene; and the target sub-imageextracted from the original image data of the scene includes thedistinct spatial area.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to some embodimentsof the present technology;

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to some embodiments of the present technology;

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to some embodiments of thepresent technology;

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to some embodiments ofthe present technology;

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to some embodiments of the presenttechnology;

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to someembodiments of the present technology;

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to some embodiments ofthe present technology;

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology;

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology;

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to embodimentsof the present technology;

FIG. 11 illustrates a flow chart of an example of a process forgenerating and using a machine-learning model according to some aspects,according to embodiments of the present technology;

FIG. 12 illustrates an example of a machine-learning model as a neuralnetwork, according to embodiments of the present technology;

FIG. 13 illustrates various aspects of the use of containers as amechanism to allocate processing, storage and/or other resources of aprocessing system to the performance of various analyses, according toembodiments of the present technology;

FIG. 14 illustrates a flow chart showing an example process of amulti-stage object classification and condition pipeline, according tosome embodiments of the present technology;

FIG. 15 illustrates an example schematic of a first stage, a secondstage, and a third stage of a multi-stage object classification andcondition pipeline, according to some embodiments of the presenttechnology;

FIG. 16 illustrates an example schematic of classifying and routingtransformed sub-images, according to some embodiments of the presenttechnology;

FIG. 17 illustrates an example schematic of using a machinelearning-based object localization model, according to some embodimentsof the present technology;

FIG. 18 illustrates an example schematic of using a digitalimage-to-object position mapping algorithm and object extractionalgorithm, according to some embodiments of the present technology;

FIG. 19 illustrates an example schematic of using a machinelearning-based object classification model and one or moreobject-condition machine learning models, according to some embodimentsof the present technology; and

FIG. 20 illustrates an example of an adapted multi-criteria lossfunction, according to some embodiments of the present technology.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionsare not intended to limit the inventions to these preferred embodiments,but rather to enable any person skilled in the art to make and use theseinventions.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the technology. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

The ensuing description provides example embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the example embodimentswill provide those skilled in the art with an enabling description forimplementing an example embodiment. It should be understood that variouschanges may be made in the function and arrangement of elements withoutdeparting from the spirit and scope of the technology as set forth inthe appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional operationsnot included in a figure. A process may correspond to a method, afunction, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination can correspond to a return ofthe function to the calling function or the main function.

Example Systems

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1 , computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10 ), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores nofor storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However, in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores no may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1 . Services provided by thecloud network can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, and/orsystems. In some embodiments, the computers, servers, and/or systemsthat make up the cloud network 116 are different from the user's ownon-premises computers, servers, and/or systems. For example, the cloudnetwork 116 may host an application, and a user may, via a communicationnetwork such as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energycommunication channel. A wired network may include a wired interface.The wired and/or wireless networks may be implemented using routers,access points, bridges, gateways, or the like, to connect devices in thenetwork 114, as will be further described with respect to FIG. 2 . Theone or more networks 108 can be incorporated entirely within or caninclude an intranet, an extranet, or a combination thereof. In oneembodiment, communications between two or more systems and/or devicescan be achieved by a secure communications protocol, such as securesockets layer (SSL) or transport layer security (TLS). In addition, dataand/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2 .

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2 , network device 204 can transmit a communicationover a network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems (e.g., an oil drillingoperation). The network devices may detect and record data related tothe environment that it monitors and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2 , computing environment 214 may includea web server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2 ) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 301-307. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 301. Physical layer 301represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 301 also defines protocols that may controlcommunications within a data transmission network.

Link layer 302 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer 302 manages node-to-nodecommunications, such as within a grid computing environment. Link layer302 can detect and correct errors (e.g., transmission errors in thephysical layer 301). Link layer 302 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 303 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 303 can also define the processes used to structure localaddressing within the network.

Transport layer 304 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 304 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 304 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 305 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 306 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 307 interacts directly with software applications andend users, and manages communications between them. Application layer307 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 321 and 322 are shown to operate inlower levels, such as physical layer 301 and link layer 302,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the link layer, and a router can operate in thenetwork layer. Inter-network connection components 323 and 328 are shownto operate on higher levels, such as layers 303-307. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3 . For example, referringback to FIG. 2 , one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a HADOOP® standard-compliant data node employing the HADOOP® DistributedFile System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, and coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project codes running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes in the grid, unique identifiers of the nodes, or theirrelationships with the primary control node) and the status of a project(including, for example, the status of each worker node's portion of theproject). The snapshot may also include analysis or results receivedfrom worker nodes in the communications grid. The backup control nodesmay receive and store the backup data received from the primary controlnode. The backup control nodes may transmit a request for such asnapshot (or other information) from the primary control node, or theprimary control node may send such information periodically to thebackup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process 500 foradjusting a communications grid or a work project in a communicationsgrid after a failure of a node, according to embodiments of the presenttechnology. The process may include, for example, receiving grid statusinformation including a project status of a portion of a project beingexecuted by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connectedto a primary control node and a worker node on a communications grid)may receive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4 , communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 include multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG. 2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However, in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DBMS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1 . Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client deice 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DBMS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DBMS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4 , data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method 700 forexecuting a project within a grid computing system, according toembodiments of the present technology. As described with respect to FIG.6 , the GESC at the control node may transmit data with a client device(e.g., client device 630) to receive queries for executing a project andto respond to those queries after large amounts of data have beenprocessed. The query may be transmitted to the control node, where thequery may include a request for executing a project, as described inoperation 702. The query can contain instructions on the type of dataanalysis to be performed in the project and whether the project shouldbe executed using the grid-based computing environment, as shown inoperation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project, asdescribed in operation 712.

As noted with respect to FIG. 2 , the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2 , and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10 , may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2 ) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2 .

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE Boo may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2 . As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2 .The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE Boo may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system woo interfacing between publishingdevice 1022 and event subscribing devices 1024 a-c, according toembodiments of the present technology. ESP system woo may include ESPdevice or subsystem 851, event publishing device 1022, an eventsubscribing device A 1024 a, an event subscribing device B 1024 b, andan event subscribing device C 1024 c. Input event streams are output toESP device 851 by publishing device 1022. In alternative embodiments,the input event streams may be created by a plurality of publishingdevices. The plurality of publishing devices further may publish eventstreams to other ESP devices. The one or more continuous queriesinstantiated by ESPE 800 may analyze and process the input event streamsto form output event streams output to event subscribing device A 1024a, event subscribing device B 1024 b, and event subscribing device C1024 c. ESP system 1000 may include a greater or a fewer number of eventsubscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9 , operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2 , data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, NorthCarolina.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11 .

In block 1102, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1104, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1106, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if, at 1108, the machine-learning model has aninadequate degree of accuracy for a particular task, the process canreturn to block 1104, where the machine-learning model can be furthertrained using additional training data or otherwise modified to improveaccuracy. However, if, at 1108. the machine-learning model has anadequate degree of accuracy for the particular task, the process cancontinue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12 . The neural network 1200 is representedas multiple layers of neurons 1208 that can exchange data between oneanother via connections 1255 that may be selectively instantiatedthereamong. The layers include an input layer 1202 for receiving inputdata provided at inputs 1222, one or more hidden layers 1204, and anoutput layer 1206 for providing a result at outputs 1277. The hiddenlayer(s) 1204 are referred to as hidden because they may not be directlyobservable or have their inputs or outputs directly accessible duringthe normal functioning of the neural network 1200. Although the neuralnetwork 1200 is shown as having a specific number of layers and neuronsfor exemplary purposes, the neural network 1200 can have any number andcombination of layers, and each layer can have any number andcombination of neurons.

The neurons 1208 and connections 1255 thereamong may have numericweights, which can be tuned during training of the neural network 1200.For example, training data can be provided to at least the inputs 1222to the input layer 1202 of the neural network 1200, and the neuralnetwork 1200 can use the training data to tune one or more numericweights of the neural network 1200. In some examples, the neural network1200 can be trained using backpropagation. Backpropagation can includedetermining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 at theoutputs 1277 and a desired output of the neural network 1200. Based onthe gradient, one or more numeric weights of the neural network 1200 canbe updated to reduce the difference therebetween, thereby increasing theaccuracy of the neural network 1200. This process can be repeatedmultiple times to train the neural network 1200. For example, thisprocess can be repeated hundreds or thousands of times to train theneural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, the connections 1255 areinstantiated and/or weighted so that every neuron 1208 only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron 1208to the next neuron 1208 in a feed-forward neural network. Such a“forward” direction may be defined as proceeding from the input layer1202 through the one or more hidden layers 1204, and toward the outputlayer 1206.

In other examples, the neural network 1200 may be a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops among the connections 1255, thereby allowing data to propagate inboth forward and backward through the neural network 1200. Such a“backward” direction may be defined as proceeding in the oppositedirection of forward, such as from the output layer 1206 through the oneor more hidden layers 1204, and toward the input layer 1202. This canallow for information to persist within the recurrent neural network.For example, a recurrent neural network can determine an output based atleast partially on information that the recurrent neural network hasseen before, giving the recurrent neural network the ability to useprevious input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer(“subsequent” in the sense of moving “forward”) of the neural network1200. Each subsequent layer of the neural network 1200 can repeat thisprocess until the neural network 1200 outputs a final result at theoutputs 1277 of the output layer 1206. For example, the neural network1200 can receive a vector of numbers at the inputs 1222 of the inputlayer 1202. The neural network 1200 can multiply the vector of numbersby a matrix of numeric weights to determine a weighted vector. Thematrix of numeric weights can be tuned during the training of the neuralnetwork 1200. The neural network 1200 can transform the weighted vectorusing a nonlinearity, such as a sigmoid tangent or the hyperbolictangent. In some examples, the nonlinearity can include a rectifiedlinear unit, which can be expressed using the equation y=max(x, 0) wherey is the output and x is an input value from the weighted vector. Thetransformed output can be supplied to a subsequent layer (e.g., a hiddenlayer 1204) of the neural network 1200. The subsequent layer of theneural network 1200 can receive the transformed output, multiply thetransformed output by a matrix of numeric weights and a nonlinearity,and provide the result to yet another layer of the neural network 1200(e.g., another, subsequent, hidden layer 1204). This process continuesuntil the neural network 1200 outputs a final result at the outputs 1277of the output layer 1206.

As also depicted in FIG. 12 , the neural network 1200 may be implementedeither through the execution of the instructions of one or more routines1244 by central processing units (CPUs), or through the use of one ormore neuromorphic devices 1250 that incorporate a set of memristors (orother similar components) that each function to implement one of theneurons 1208 in hardware. Where multiple neuromorphic devices 1250 areused, they may be interconnected in a depth-wise manner to enableimplementing neural networks with greater quantities of layers, and/orin a width-wise manner to enable implementing neural networks havinggreater quantities of neurons 1208 per layer.

The neuromorphic device 1250 may incorporate a storage interface 1299 bywhich neural network configuration data 1293 that is descriptive ofvarious parameters and hyper parameters of the neural network 1200 maybe stored and/or retrieved. More specifically, the neural networkconfiguration data 1293 may include such parameters as weighting and/orbiasing values derived through the training of the neural network 1200,as has been described. Alternatively, or additionally, the neuralnetwork configuration data 1293 may include such hyperparameters as themanner in which the neurons 1208 are to be interconnected (e.g.,feed-forward or recurrent), the trigger function to be implementedwithin the neurons 1208, the quantity of layers and/or the overallquantity of the neurons 1208. The neural network configuration data 1293may provide such information for more than one neuromorphic device 1250where multiple ones have been interconnected to support larger neuralnetworks.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedilyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). Such processors may also provide an energy savingswhen compared to generic CPUs. For example, some of these processors caninclude a graphical processing unit (GPU), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), anartificial intelligence (AI) accelerator, a neural computing core, aneural computing engine, a neural processing unit, a purpose-built chiparchitecture for deep learning, and/or some other machine-learningspecific processor that implements a machine learning approach or one ormore neural networks using semiconductor (e.g., silicon (Si), galliumarsenide (GaAs)) devices. These processors may also be employed inheterogeneous computing architectures with a number of and/or a varietyof different types of cores, engines, nodes, and/or layers to achievevarious energy efficiencies, processing speed improvements, datacommunication speed improvements, and/or data efficiency targets andimprovements throughout various parts of the system when compared to ahomogeneous computing architecture that employs CPUs for general purposecomputing.

FIG. 13 illustrates various aspects of the use of containers 1336 as amechanism to allocate processing, storage and/or other resources of aprocessing system 1300 to the performance of various analyses. Morespecifically, in a processing system 1300 that includes one or more nodedevices 1330 (e.g., the aforedescribed grid system 400), the processing,storage and/or other resources of each node device 1330 may be allocatedthrough the instantiation and/or maintenance of multiple containers 1336within the node devices 1330 to support the performance(s) of one ormore analyses. As each container 1336 is instantiated, predeterminedamounts of processing, storage and/or other resources may be allocatedthereto as part of creating an execution environment therein in whichone or more executable routines 1334 may be executed to cause theperformance of part or all of each analysis that is requested to beperformed.

It may be that at least a subset of the containers 1336 are eachallocated a similar combination and amounts of resources so that each isof a similar configuration with a similar range of capabilities, andtherefore, are interchangeable. This may be done in embodiments in whichit is desired to have at least such a subset of the containers 1336already instantiated prior to the receipt of requests to performanalyses, and thus, prior to the specific resource requirements of eachof those analyses being known.

Alternatively, or additionally, it may be that at least a subset of thecontainers 1336 are not instantiated until after the processing system1300 receives requests to perform analyses where each request mayinclude indications of the resources required for one of those analyses.Such information concerning resource requirements may then be used toguide the selection of resources and/or the amount of each resourceallocated to each such container 1336. As a result, it may be that oneor more of the containers 1336 are caused to have somewhat specializedconfigurations such that there may be differing types of containers tosupport the performance of different analyses and/or different portionsof analyses.

It may be that the entirety of the logic of a requested analysis isimplemented within a single executable routine 1334. In suchembodiments, it may be that the entirety of that analysis is performedwithin a single container 1336 as that single executable routine 1334 isexecuted therein. However, it may be that such a single executableroutine 1334, when executed, is at least intended to cause theinstantiation of multiple instances of itself that are intended to beexecuted at least partially in parallel. This may result in theexecution of multiple instances of such an executable routine 1334within a single container 1336 and/or across multiple containers 1336.

Alternatively, or additionally, it may be that the logic of a requestedanalysis is implemented with multiple differing executable routines1334. In such embodiments, it may be that at least a subset of suchdiffering executable routines 1334 are executed within a singlecontainer 1336. However, it may be that the execution of at least asubset of such differing executable routines 1334 is distributed acrossmultiple containers 1336.

Where an executable routine 1334 of an analysis is under development,and/or is under scrutiny to confirm its functionality, it may be thatthe container 1336 within which that executable routine 1334 is to beexecuted is additionally configured assist in limiting and/or monitoringaspects of the functionality of that executable routine 1334. Morespecifically, the execution environment provided by such a container1336 may be configured to enforce limitations on accesses that areallowed to be made to memory and/or I/O addresses to control whatstorage locations and/or I/O devices may be accessible to thatexecutable routine 1334. Such limitations may be derived based oncomments within the programming code of the executable routine 1334and/or other information that describes what functionality theexecutable routine 1334 is expected to have, including what memoryand/or I/O accesses are expected to be made when the executable routine1334 is executed. Then, when the executable routine 1334 is executedwithin such a container 1336, the accesses that are attempted to be madeby the executable routine 1334 may be monitored to identify any behaviorthat deviates from what is expected.

Where the possibility exists that different executable routines 1334 maybe written in different programming languages, it may be that differentsubsets of containers 1336 are configured to support differentprogramming languages. In such embodiments, it may be that eachexecutable routine 1334 is analyzed to identify what programminglanguage it is written in, and then what container 1336 is assigned tosupport the execution of that executable routine 1334 may be at leastpartially based on the identified programming language. Where thepossibility exists that a single requested analysis may be based on theexecution of multiple executable routines 1334 that may each be writtenin a different programming language, it may be that at least a subset ofthe containers 1336 are configured to support the performance of variousdata structure and/or data format conversion operations to enable a dataobject output by one executable routine 1334 written in one programminglanguage to be accepted as an input to another executable routine 1334written in another programming language.

As depicted, at least a subset of the containers 1336 may beinstantiated within one or more VMs 1331 that may be instantiated withinone or more node devices 1330. Thus, in some embodiments, it may be thatthe processing, storage and/or other resources of at least one nodedevice 1330 may be partially allocated through the instantiation of oneor more VMs 1331, and then in turn, may be further allocated within atleast one VM 1331 through the instantiation of one or more containers1336.

In some embodiments, it may be that such a nested allocation ofresources may be carried out to effect an allocation of resources basedon two differing criteria. By way of example, it may be that theinstantiation of VMs 1331 is used to allocate the resources of a nodedevice 1330 to multiple users or groups of users in accordance with anyof a variety of service agreements by which amounts of processing,storage and/or other resources are paid for each such user or group ofusers. Then, within each VM 1331 or set of VMs 1331 that is allocated toa particular user or group of users, containers 1336 may be allocated todistribute the resources allocated to each VM 1331 among variousanalyses that are requested to be performed by that particular user orgroup of users.

As depicted, where the processing system 1300 includes more than onenode device 1330, the processing system 1300 may also include at leastone control device 1350 within which one or more control routines 1354may be executed to control various aspects of the use of the nodedevice(s) 1330 to perform requested analyses. By way of example, it maybe that at least one control routine 1354 implements logic to controlthe allocation of the processing, storage and/or other resources of eachnode device 1300 to each VM 1331 and/or container 1336 that isinstantiated therein. Thus, it may be the control device(s) 1350 thateffects a nested allocation of resources, such as the aforedescribedexample allocation of resources based on two differing criteria.

As also depicted, the processing system 1300 may also include one ormore distinct requesting devices 1370 from which requests to performanalyses may be received by the control device(s) 1350. Thus, and by wayof example, it may be that at least one control routine 1354 implementslogic to monitor for the receipt of requests from authorized usersand/or groups of users for various analyses to be performed using theprocessing, storage and/or other resources of the node device(s) 1330 ofthe processing system 1300. The control device(s) 1350 may receiveindications of the availability of resources, the status of theperformances of analyses that are already underway, and/or still otherstatus information from the node device(s) 1330 in response to polling,at a recurring interval of time, and/or in response to the occurrence ofvarious preselected events. More specifically, the control device(s)1350 may receive indications of status for each container 1336, each VM1331 and/or each node device 1330. At least one control routine 1354 mayimplement logic that may use such information to select container(s)1336, VM(s) 1331 and/or node device(s) 1330 that are to be used in theexecution of the executable routine(s) 1334 associated with eachrequested analysis.

As further depicted, in some embodiments, the one or more controlroutines 1354 may be executed within one or more containers 1356 and/orwithin one or more VMs 1351 that may be instantiated within the one ormore control devices 1350. It may be that multiple instances of one ormore varieties of control routine 1354 may be executed within separatecontainers 1356, within separate VMs 1351 and/or within separate controldevices 1350 to better enable parallelized control over parallelperformances of requested analyses, to provide improved redundancyagainst failures for such control functions, and/or to separatediffering ones of the control routines 1354 that perform differentfunctions. By way of example, it may be that multiple instances of afirst variety of control routine 1354 that communicate with therequesting device(s) 1370 are executed in a first set of containers 1356instantiated within a first VM 1351, while multiple instances of asecond variety of control routine 1354 that control the allocation ofresources of the node device(s) 1330 are executed in a second set ofcontainers 1356 instantiated within a second VM 1351. It may be that thecontrol of the allocation of resources for performing requested analysesmay include deriving an order of performance of portions of eachrequested analysis based on such factors as data dependenciesthereamong, as well as allocating the use of containers 1336 in a mannerthat effectuates such a derived order of performance.

Where multiple instances of control routine 1354 are used to control theallocation of resources for performing requested analyses, such as theassignment of individual ones of the containers 1336 to be used inexecuting executable routines 1334 of each of multiple requestedanalyses, it may be that each requested analysis is assigned to becontrolled by just one of the instances of control routine 1354. Thismay be done as part of treating each requested analysis as one or more“ACID transactions” that each have the four properties of atomicity,consistency, isolation and durability such that a single instance ofcontrol routine 1354 is given full control over the entirety of eachsuch transaction to better ensure that either all of each suchtransaction is either entirely performed or is entirely not performed.As will be familiar to those skilled in the art, allowing partialperformances to occur may cause cache incoherencies and/or datacorruption issues.

As additionally depicted, the control device(s) 1350 may communicatewith the requesting device(s) 1370 and with the node device(s) 1330through portions of a network 1399 extending thereamong. Again, such anetwork as the depicted network 1399 may be based on any of a variety ofwired and/or wireless technologies, and may employ any of a variety ofprotocols by which commands, status, data and/or still other varietiesof information may be exchanged. It may be that one or more instances ofa control routine 1354 cause the instantiation and maintenance of a webportal or other variety of portal that is based on any of a variety ofcommunication protocols, etc. (e.g., a restful API). Through such aportal, requests for the performance of various analyses may be receivedfrom requesting device(s) 1370, and/or the results of such requestedanalyses may be provided thereto. Alternatively, or additionally, it maybe that one or more instances of a control routine 1354 cause theinstantiation of and maintenance of a message passing interface and/ormessage queues. Through such an interface and/or queues, individualcontainers 1336 may each be assigned to execute at least one executableroutine 1334 associated with a requested analysis to cause theperformance of at least a portion of that analysis.

Although not specifically depicted, it may be that at least one controlroutine 1354 may include logic to implement a form of management of thecontainers 1336 based on the Kubernetes container management platformpromulgated by Could Native Computing Foundation of San Francisco, CA,USA. In such embodiments, containers 1336 in which executable routines1334 of requested analyses may be instantiated within “pods” (notspecifically shown) in which other containers may also be instantiatedfor the execution of other supporting routines. Such supporting routinesmay cooperate with control routine(s) 1354 to implement a communicationsprotocol with the control device(s) 1350 via the network 1399 (e.g., amessage passing interface, one or more message queues, etc.).Alternatively, or additionally, such supporting routines may serve toprovide access to one or more storage repositories (not specificallyshown) in which at least data objects may be stored for use inperforming the requested analyses.

Associated Processes

Method for Configuring and Using a Multi-Stage Object Classification andCondition Pipeline

FIG. 14 illustrates one embodiment of a method 1400 for configuring andusing a multi-stage object classification and condition pipeline. Itwill be appreciated that other embodiments contemplated within the scopeof the present disclosure may involve more operations, fewer operations,different operations, or a different order of operations than as shownin FIG. 14 .

As described in more detail herein, a system or service implementing themethod 1400 may use the multi-stage object classification and conditionpipeline 1501 to predict a probable object class and/or a probableobject-condition class for target objects detected within a subjectscene. The multi-stage object classification and condition pipeline1501, in one or more embodiments, may include a first stage 1532 thatincludes using a machine learning-based object localization model 1508,a second stage 1534 that includes using a machine learning-based objectclassification model 1524, and a third stage 1536 that includes using atleast one object-condition machine learning classification model 1528.

Generating the Machine Learning-Based Object Localization Model

In one or more embodiments, the system or service implementing themethod 1400 may function to generate the machine learning-based objectlocalization model 1508 using any suitable machine learning modeltraining and testing platform.

As described in more detail herein, in one or more embodiments, themachine learning-based object localization model 1508, when trained, mayfunction to identify (e.g., detect and locate) electrical componentswithin a downsampled digital image of an electrical grid that correspondto one or more target electrical component object classes. For instance,in the non-limiting example of FIG. 17 , the machine learning-basedobject localization model 1508, when trained, may function to identifyeach object (e.g., each electrical insulator) within a subjectdownsampled digital image of an electrical grid that likely correspondsto an electrical insulator object class. Additionally, or alternatively,in such a non-limiting example, the machine learning-based objectlocalization model 1508, when trained, may function to identify eachobject (e.g., each electrical transformer) within a subject downsampleddigital image of an electrical grid that likely corresponds to anelectrical transformer object class.

In one or more embodiments, generating the machine learning-based objectlocalization model 1508 may include obtaining an object detectionmachine learning model (e.g., YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, FASTR-CNN, or any suitable object detection machine learning model). Forinstance, in a non-limiting example, the system or service implementingthe method 1400 may function to obtain a YOLOv2 object detection machinelearning model that may have used the multi-criteria loss function 2010during a model training. The multi-criteria loss function 2010, in someembodiments, may include a bounding box coordinate regression errorcomponent 2012, a bounding box score prediction error component 2014,and an object classification error component 2016.

It shall be recognized that the bounding box coordinate regression errorcomponent 2012 may be used to evaluate a proficiency of a target machinelearning model (e.g., YOLOv2 object detection machine learning model) inpredicting bounding boxes of objects present within a subject imageduring a training of the target machine learning model. Specifically,the bounding box coordinate regression error component 2012 may usesum-squared error (SSE) to measure a difference between predictedbounding box coordinates and ground-truth bounding box coordinates. Itshall be recognized that λ_(coord) relates to a hyperparameter thatcontrols a contribution of a localization loss associated with thebounding box coordinate regression error component 2012 with respect tothe bounding box score prediction error component 2014 and the objectclassification error component 2016, S relates to a number of grid cellsalong each image direction, B relates to a number of bounding boxespredicted by each grid cell, L_(ij) ^(obj) is an indicator function thatis equal to one (1) when the j-th bounding box in the i-th grid cell isresponsible for the object and is equal to zero (0) otherwise,

,

,

,

relate to predicted coordinates and predicted dimensions (e.g.,predicted x, predicted y, predicted width, and predicted height) of asubject bounding box, and

,

,

,

relate to ground-truth coordinates and dimensions (e.g., ground-truth x,ground-truth y, ground-truth width, and ground-truth height) of thesubject bounding box.

It shall be further recognized that the bounding box score predictionerror component 2014 may be used to evaluate a proficiency of a targetmachine learning model (e.g., YOLOv2 object detection machine learningmodel) in predicting whether a subject object is present in a subjectgrid cell and an accuracy of a corresponding confidence score associatedwith the subject object during a training of the target machine learningmodel. Specifically, the confidence score represents the intersectionover union (IoU) between a subject predicted bounding box and acorresponding ground-truth bounding box. For instance, when a subjectgrid cell is not responsible for predicting a subject object, thebounding box score prediction error component 2014 may penalize thetarget machine learning model. It shall be recognized that C_(ij)relates to the predicted confidence score for the i-th cell and thecorresponding j-th bounding box, λ_(noobj) relates to a hyperparameterthat balances the confidence score loss associated with empty grid cellsrelative to the confidence score loss associated with grid cellscontaining objects, L_(ij) ^(noobj) is an indicator function that isequal to one (1) when the j-th bounding box in the i-th cell is notresponsible for any object and zero (0) otherwise, and λ_(obj) relatesto a hyperparameter that that controls a contribution of the lossassociated with the bounding box score prediction error component 2014during a training of the target machine learning model.

It shall be further recognized that the object classification errorcomponent 2016 may be used to evaluate a proficiency of a target machinelearning model (e.g., YOLOv2 object detection machine learning model) inpredicting object class predictions of objects within a subject imageduring a training of the target machine learning model. It shall berecognized that p_(ij)(c) relates to the predicted probability of thec-th object class for the i-th grid cell,

relates to the ground-truth object class of the object associated withthe i-th grid cell, c∈classes relates to the total number of objectclasses, and λ_(class) relates to a hyperparameter that that controls acontribution of the loss associated with object classification errorcomponent 2016 during a training of the target machine learning model.

Additionally, in one or more embodiments, generating the machinelearning-based object localization model 1508 may include adapting themulti-criteria loss function 2010 of the object detection machinelearning model to remove one or more loss function components associatedwith object classification. For instance, in a non-limiting example, themulti-criteria loss function 2010 of the YOLOv2 object detection machinelearning model may be adapted to a target multi-criteria loss function2020 based on eliminating one or more loss function components of themulti-criteria loss function 2010 associated with object classification.Stated another way, the multi-criteria loss function 2010 of the YOLOv2object detection machine learning model may include the objectclassification error component 2016 and the target multi-criteria lossfunction 2020 may exclude the object classification error component2016, as shown generally by way of example in FIG. 20 .

Additionally, in one or more embodiments, generating the machinelearning-based object localization model 1508 may include transformingthe object detection machine learning model to the machinelearning-based object localization model 1508 based on implementing(e.g., using or the like) the target multi-criteria loss function 2020(instead of the multi-criteria loss function 2010) during a training ofthe object detection machine learning model. That is, in one or moreembodiments, transforming the object detection machine learning model tothe machine learning-based object localization model 1508 may include atransformation of a model structure of the object detection machinelearning model based on or in response to a training of the objectdetection machine learning model. For instance, in a non-limitingexample, the YOLOv2 object detection machine learning model may betrained on a plurality of training data samples that correspond to atarget object class (e.g., the electrical insulator class) using, inpart, the target multi-criteria loss function 2020. Accordingly, basedon the training, the machine learning-based object localization model1508 may function to identify and locate target objects within a subjectscene that likely correspond to the target object class (e.g., theelectrical insulator class). In a variant to such non-limiting example,the YOLOv2 object detection machine learning model may be trained on acorpus of training data samples that may include a first set of trainingdata samples that correspond to a first object class (e.g., theelectrical insulator class), a second set of training data samples thatcorrespond to a second object class (e.g., the electrical transformerclass), and/or a third set of training data samples that correspond tothe first object class (e.g., the electrical insulator class) and thesecond object class (e.g., the electrical transformer class) using, inpart, the target multi-criteria loss function 2020. Accordingly, basedon the training, the machine learning-based object localization model1508 may function to identify and locate target objects within a subjectscene that likely correspond to at least one of the first object class(e.g., the electrical insulator class) and the second object class(e.g., the electrical transformer class).

It shall be recognized that the object detection machine learning modelmay be trained on training data samples that correspond to additionalobject classes, fewer object classes, and/or different object classeswithout departing from the scope of the disclosure.

It shall be further recognized that the system or service implementingthe method 1400 may function to replace the multi-criteria loss function2010 with the target multi-criteria loss function 2020 within a modeltraining structure of the YOLOv2 object detection machine learning modelprior to a training of the YOLOv2 object detection machine learningmodel.

Generating the Machine Learning-Based Object Classification Model

In one or more embodiments, the system or service implementing themethod 1400 may function to generate the machine learning-based objectclassification model 1524 using any suitable machine learning modeltraining and testing platform.

In one or more embodiments, the machine learning-based objectclassification model 1524 may be generated based on a training of atarget machine learning-based image classification model using an objectclassification training data corpus.

In one or more embodiments, the object classification training datacorpus may include a plurality of training data samples in which a firstsubset of the plurality of training data samples correspond to anin-scope object class (e.g., electrical insulator class) and a secondsubset of the plurality of training data samples correspond to anout-of-scope object class (e.g., not applicable class). Accordingly,based on the training of the target machine learning-based imageclassification model with the object classification training datacorpus, the machine learning-based object classification model 1524 maybe configured to classify a subject sub-image containing a subjectobject to the in-scope object class (e.g., electrical insulator class)or the out-of-scope object class (e.g., not applicable class) based onthe machine learning-based object classification model 1524 receivingthe subject sub-image. As described in more detail herein, a sub-imagemay be a target portion or target segment of a subject original digitalimage, as shown generally by way of example in FIG. 18 .

Additionally, or alternatively, in one or more embodiments, the objectclassification training data corpus may include a plurality of trainingdata samples in which a first subset of the plurality of training datasamples correspond to a first in-scope object class (e.g., electricalinsulator class), a second subset of the plurality of training datasamples correspond to a second in-scope object class (e.g., electricaltransformer class), and a third subset of the plurality of training datasamples correspond to an out-of-scope object class (e.g., not applicableclass). Accordingly, based on the training of the target machinelearning-based image classification model using the objectclassification training data corpus, the machine learning-based objectclassification model 1524 may be configured to classify a subjectsub-image containing a subject object to the first in-scope object class(e.g., electrical insulator class), the second in-scope object class(e.g., electrical transformer class), or the out-of-scope object class(e.g., not applicable class) based on the machine learning-based objectclassification model 1524 receiving the subject sub-image.

Generating one or more Object-Condition Machine Learning ClassificationModels

In one or more embodiments, the system or service implementing themethod 1400 may function to generate one or more object-conditionmachine learning classification models using any suitable machinelearning model training and testing platform.

An object-condition machine learning classification model, as generallyreferred to herein, may be a sub-classifier that is trained to identifyspecific conditions or characteristics of a target object class (e.g.,an in-scope object class or the like) of the machine learning-basedobject classification model 1524. Stated another way, anobject-condition machine learning classification model may be trained toclassify a sub-image associated with a subject object to one or moresub-classes of the target object class to which the object-conditionmachine learning classification model corresponds.

For instance, in a non-limiting example, an insulator object-conditionmodel 1920 may be trained to identify specific conditions orcharacteristics (e.g., sub-classes) of the electrical insulator classsuch as whether an electrical insulator (e.g., object) contained withina subject sub-image is defective or not. In such non-limiting example,the system or service implementing the method 1400 may function togenerate the insulator object-condition model 1920 based on a trainingof a machine learning-based image classification model using a trainingdata corpus that may include a first set of training data samples thatcorrespond to a first type of electrical insulator defect class (e.g.,electrical insulators having one or more broken shells), a second set oftraining data samples that correspond to a second type of electricalinsulator defect class (e.g., electrical insulators having shockdamage), and/or a third set of training data samples that correspond toa not defective electrical insulator class (i.e., electrical insulatorsthat are not visibly defective).

Accordingly, based on the training, the insulator object-condition model1920 may be configured to classify a subject sub-image of a subjectobject to the first type of electrical insulator defect class, thesecond type of electrical insulator defect class, or the not defectiveelectrical insulator class based on the insulator object-condition model1920 receiving the subject sub-image.

In another non-limiting example, the transformer object-condition model1918 may be trained to identify specific conditions or characteristics(e.g., sub-classes) of the electrical transformer class in analogousways.

It shall be recognized that, in some embodiments, one or more of theobject-condition machine learning models may be trained using aregression estimation model (e.g., linear regression model, etc.)without departing from the scope of the disclosure.

It shall be further recognized that the system or service implementingthe method 1400 may use the multi-stage object classification andcondition pipeline 1501 in a variety of modes. For instance, in a firstimplementation, based on receiving n-number of original digital images,the system or service implementing the method 1400 may function toimplement n-number of multi-stage object classification and conditionpipelines to concurrently process each of the n-number of originaldigital images.

Additionally, or alternatively, in a second implementation, based onreceiving n-number of original digital images, the system or serviceimplementing the method 1400 may function to implement a singlemulti-stage object classification and condition pipeline that isconfigured to sequentially process each of the n-number of originaldigital images.

First Stage of the Multi-Stage Object Classification and ConditionPipeline

In one or more embodiments, the method 1400 may include process 1410.Process 1410 may be used within the first stage 1532 of the multi-stageobject classification and condition pipeline 1501. The first stage 1532,in one or more embodiments, may receive an original digital image 1502of a subject scene and output a distinct sub-image of the originaldigital image 1502 for each target object detected within the subjectscene, as described in more detail herein. It shall be recognized thatthe “first stage” may be interchangeably referred to herein as a “firstmodeling stage”, a “localization stage”, or the like.

In one or more embodiments, the original digital image 1502 may beassociated with or may correspond to a digital frame of a digital video.For instance, in a non-limiting example, a digital video capturingdevice (e.g., a digital video capturing device coupled to a drone or thelike) may be used to capture a digital video of a physical environmentand, subsequently, the system or service implementing the method 1400may function to receive the digital video. In such non-limiting example,the digital video capturing device may function to capture a digitalvideo of one or more components associated with an electrical grid, suchas one or more electrical transformers, one or more transmission towers,one or more substations, one or more wires, one or more conductors, oneor more electrical insulators, and/or the like. It shall be recognizedthat in such non-limiting example, the digital video may capture the oneor more electrical transformers, the one or more transmission towers,the one or more substations, the one or more wires, the one or moreconductors, the one or more electrical insulators, and/or the like at avariety of positions, distances, and/or angles, which traditionallycauses problems with accurately assessing such objects. However, asdescribed in more detail herein, a system or service using themulti-stage object classification and condition pipeline may function toaccurately predict a probable object class and/or a probableobject-condition class of such objects.

Accordingly, based on receiving the digital video, the system or serviceimplementing the method 1400 may function to convert the digital videoto a sequence of original digital images in which each original digitalimage of the sequence of original digital images may correspond to adistinct digital frame of the digital video. In such non-limitingexample, the original digital image 1502 illustrated in FIG. 15 may beassociated with or may correspond to the first original digital image inthe sequence of original digital images. Alternatively, in anothernon-limiting example, the original digital image 1502 illustrated inFIG. 15 may be associated with or may correspond to the second originaldigital image in the sequence of original digital images. It shall berecognized that the original digital image 1502 may correspond to anyoriginal digital image within the sequence of original digital imageswithout departing from the scope of the present disclosure.

In one or more embodiments, the original digital image 1502 may berouted to a digital image downsampling algorithm 1504 that may functionto decrease (e.g., reduce) an image resolution of the original digitalimage 1502 to a target image resolution. In such embodiments, based onthe digital image downsampling algorithm 1504 receiving the originaldigital image 1502, the digital image downsampling algorithm 1504 mayfunction to output a downsampled digital image 1506 of the originaldigital image 1502. Stated another way, in one or more embodiments, thedigital image downsampling algorithm 1504 may function to receive, asinput, digital image data of the original digital image 1502 and outputdownsampled image data that corresponds to the downsampled digital image1506.

For instance, in a non-limiting example, the original digital image 1502that may be provided, as input, to the digital image downsamplingalgorithm 1504 may include digital image data associated with a targetscene, as shown generally by way of example in FIG. 17 . A scene, asgenerally referred to herein, may relate to a view of a real-worldenvironment that may contain multiple surfaces and objects that mayoptionally be organized in a meaningful way. For instance, in theexample of FIG. 17 , the scene of the original digital image 1502relates to an ariel view that includes a portion of an electrical grid(e.g., an area of interest). Accordingly, based on the digital imagedownsampling algorithm 1504 receiving the original digital image 1502,the digital image downsampling algorithm 1504 may function to output adownsampled digital image 1506 of the original digital image 1502. Thatis, in one or more embodiments, the downsampled digital image 1506 mayhave an image resolution that is lower than the image resolution of theoriginal digital image 1502.

In one or more embodiments, based on or in response to the digital imagedownsampling algorithm 1504 outputting the downsampled digital image1506, the downsampled digital image 1506 may be provided, as input, tothe machine learning-based object localization model 1508. Statedanother way, the digital image data of the original digital image 1502may have a first image resolution and the digital image data of thedownsampled digital image 1506 may have a second image resolution lowerthan the first image resolution. It shall be recognized that the digitalimage downsampling algorithm 1504 may be configured to output thedownsampled digital image 1506 with an image resolution that satisfiesan input image resolution criterion (e.g., a target image resolutionparameter or the like) of the machine learning-based object localizationmodel 1508.

Accordingly, based on the machine learning-based object localizationmodel 1508 receiving the downsampled digital image 1506, process 1410may be commenced. In some embodiments, process 1410 may includedetecting, via the machine learning-based object localization model1508, one or more target objects within a subject scene based on digitalimage data of the downsampled digital image 1506. In such embodiments,the machine learning-based object localization model 1508 may be trainedto identify each object within the subject scene of the downsampleddigital image 1506 that likely corresponds to a target object class thatthe machine learning-based object localization model 1508 was trained toidentify and, accordingly, compute object position data (e.g., boundingbox data, object location data, etc.) that includes such object, asshown generally by way of example in FIG. 15 and FIG. 17 .

For instance, in the non-limiting example of FIG. 15 , based on themachine learning-based object localization model 1508 receiving thedownsampled digital image 1506, the machine learning-based objectlocalization model 1508 may identify n-number of objects within thescene of the downsampled digital image 1506 based on the machinelearning-based object localization model 1508 assessing the digitalimage data of the downsampled digital image 1506. In such non-limitingexample, the machine learning-based object localization model 1508 mayfunction to identify that a subject object (e.g., object A) likelycorresponds to a target object class that the machine learning-basedobject localization model 1508 was trained to identify and, accordingly,compute object position data associated with the subject object (e.g.,object position data of object A 1510A) that defines an approximatespatial area (e.g., an approximate bounding box) of the subject objectwithin the digital image data of the downsampled digital image 1506.Additionally, or alternatively, in such non-limiting example, themachine learning-based object localization model 1508 may function toidentify that a subject object (e.g., object B) likely corresponds to atarget object class that the machine learning-based object localizationmodel 1508 was trained to identify and, accordingly, compute objectposition data associated with the subject object (e.g., object positiondata of object B 1510B) that defines an approximate spatial area (e.g.,an approximate bounding box) of the subject object within the digitalimage data of the downsampled digital image 1506. Additionally, oralternatively, in such non-limiting example, the machine learning-basedobject localization model 1508 may function to identify that a subjectobject (e.g., object N) likely corresponds to a target object class thatthe machine learning-based object localization model 1508 was trained toidentify and, accordingly, compute object position data associated withthe subject object (e.g., object position data of object N 1510N) thatdefines an approximate spatial area (e.g., an approximate bounding box)of the subject object within the digital image data of the downsampleddigital image 1506. It shall be noted that, in one or more embodiments,the computed object position data that corresponds to a subject objectmay include bounding box data, such as a bounding box height, a boundingbox width, and/or a bounding box coordinate that defines an origin ofthe bounding box from which the bounding box height and the bounding boxwidth may be referenced.

Stated another way, the machine learning-based object localization model1508 may be trained to detect each object within the scene of thedownsampled digital image 1506 that likely corresponds to a targetobject class (e.g., an electrical insulator class) and, in turn, computeobject position data for each detected object within the scene. Forinstance, in the non-limiting example of FIG. 17 , in response to themachine learning-based object localization model 1508 receiving thedownsampled digital image 1506, the machine learning-based objectlocalization model 1508 may identify that the downsampled digital image1506 includes n-number of objects (e.g., one or more objects, two ormore objects, three or more objects, or any number of objects) thatlikely correspond to the target object class (e.g., the electricalinsulator class) and, in turn, compute object position data for each ofthe n-number of objects. Specifically, the machine learning-based objectlocalization model 1508 may function to detect ten (10) objects (e.g.,object 1710A, object 1710B, object 1710C, object 1710D, object 1710E,object 1710F, object 1710G, object 1710H, object 1710I, and object1710N) that likely correspond to the electrical insulator class and,accordingly, compute object position data that defines ten (10)approximate spatial areas (e.g., bounding box 1712A, bounding box 1712B,bounding box 1712C, bounding box 1712D, bounding box 1712E, bounding box1712F, bounding box 1712G, bounding box 1712H, bounding box 1712I, andbounding box 1710N) that correspond to the ten (10) objects,respectively.

In a variant to such non-limiting example, the machine learning-basedobject localization model 1508 may be trained to detect each object withthe scene of the downsampled digital image 1506 that likely correspondsto any one of a plurality of object classes (e.g., electrical insulatorclass, electrical transformer class, etc.) and, in turn, define objectposition data for each detected object. In such non-limiting example,based on the machine learning-based object localization model 1508receiving the downsampled digital image 1506, the machine learning-basedobject localization model 1508 may detect n-number of objects (e.g., oneor more objects, two or more objects, three or more objects, etc.) thatlikely correspond to a first object class (e.g., the electricalinsulator class) of the plurality of object classes and n-number ofobjects (e.g., one or more objects, two or more objects, three or moreobjects, etc.) that likely correspond to a second object class (e.g.,the electrical transformer class) of the plurality of object classes.Accordingly, the machine learning-based object localization model 1508may function to define a distinct spatial area (e.g., bounding box) foreach object detected by the machine learning-based object localizationmodel 1508 in analogous ways described above.

In one or more embodiments, the object position data computed by themachine learning-based object localization model 1508 may be provided,as input, to a digital image-to-object position mapping model 1512. Insuch embodiments, based on the digital image-to-object position mappingmodel 1512 receiving the object position data, process 1420 may becommenced. Process 1420, in one or more embodiments, may includeidentifying a likely position of each target object detected by themachine learning-based object localization model 1508 within digitalimage data of the original digital image 1502. Stated another way, thedigital image-to-object position mapping model 1512 may function to mapthe object position data outputted (e.g., computed, predicted, etc.) bythe machine learning-based object localization model 1508 to theoriginal digital image 1502. It shall be noted that the object positiondata predicted by the machine learning-based object localization model1508 may be mapped to the original digital image 1502 using any suitabletechnique, such as coordinate normalization or the like.

In one or more embodiments, based on the digital image-to-objectposition mapping model 1512 receiving the object position data computedby the machine learning-based object localization model 1508 and/or theoriginal digital image 1502, the digital image-to-object positionmapping model 1512 may function to output an original digitalimage-to-object position data mapping 1514, as shown generally by way ofexample in FIG. 15 . The original digital image-to-object position datamapping 1514, in some embodiments, may correlate the object positiondata associated with the downsampled digital image 1506 to the originaldigital image 1502.

For instance, in the non-limiting example of FIG. 18 , bounding box1712A may include object A 1710A within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712A maycorrespond to bounding box 1812A, which may include object A 1710Awithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712B may include object B 1710B within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712B maycorrespond to bounding box 1812B, which may include object B 1710Bwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712C may include object C 1710C within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712C maycorrespond to bounding box 1812C, which may include object C 1710Cwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712D may include object D 1710D within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712D maycorrespond to bounding box 1812D, which may include object D 1710Dwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712E may include object E 1710E within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712E maycorrespond to bounding box 1812E, which may include object E 1710Ewithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712F may include object F 1710F within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712F maycorrespond to bounding box 1812F, which may include object F 1710Fwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712G may include object G 1710G within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712G maycorrespond to bounding box 1812G, which may include object G 1710Gwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712H may include object H 1710H within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712H maycorrespond to bounding box 1812H, which may include object H 1710Hwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712I may include object I 1710I within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712I maycorrespond to bounding box 1812I, which may include object I 1710Iwithin digital image data of the original digital image 1502.

Additionally, or alternatively, in such non-limiting example, boundingbox 1712N may include object N 1710N within digital image data of thedownsampled digital image 1506. Accordingly, bounding box 1712N maycorrespond to bounding box 1812N, which may include object N 1710Nwithin digital image data of the original digital image 1502.

In one or more embodiments, the original digital image-to-objectposition data mapping 1514 may be provided, as input, to an objectextraction model 1516. In such embodiments, based on the objectextraction model 1516 receiving the original digital image-to-objectposition data mapping 1514, process 1430 may be commenced. Process 1430,in one or more embodiments, may include extracting, from digital imagedata of the original digital image 1502, a distinct sub-image for eachtarget object detected by the machine learning-based object localizationmodel 1508 based on the likely position of a subject object within thedigital image data of the original digital image 1502.

Stated another way, in one or more embodiments, process 1420 mayfunction to identify a likely position of a subject object detected bythe machine learning-based object localization model 1508 within digitalimage data of the original digital image 1502, and process 1430 mayfunction to extract, from digital image data of the original digitalimage 1502, a sub-image containing the subject object based on thelikely position.

For instance, in the non-limiting example of FIG. 15 , the objectposition data of object A 1510A may define an approximate spatial area(e.g., an approximate bounding box) of object A within the digital imagedata of the downsampled digital image 1506. In such non-limitingexample, process 1420 may function to identify a likely position ofobject A within the digital image data of the original digital image1502. In some embodiments, identifying the likely position of object Awithin the digital image data of the original digital image 1502 mayinclude correlating, via one or more processors, the approximate spatialarea of object A within the digital image data of the downsampleddigital image 1506 to a distinct spatial area in digital image data ofthe original digital image 1502. Additionally, in such non-limitingexample, process 1430 may function to extract, from the digital imagedata of the original digital image 1502, a sub-image of object A 1518Abased on the likely position of object A (e.g., the sub-image of objectA 1518A may include the distinct spatial area).

Additionally, or alternatively, in the non-limiting example of FIG. 15 ,the object position data of object B 1510B may define an approximatespatial area (e.g., an approximate bounding box) of object B within thedigital image data of the downsampled digital image 1506. In suchnon-limiting example, process 1420 may function to identify a likelyposition of object B within the digital image data of the originaldigital image 1502. In some embodiments, identifying the likely positionof object B within the digital image data of the original digital image1502 may include correlating, via one or more processors, theapproximate spatial area of object B within the digital image data ofthe downsampled digital image 1506 to a distinct spatial area in digitalimage data of the original digital image 1502. Additionally, in suchnon-limiting example, process 1430 may function to extract, from thedigital image data of the original digital image 1502, a sub-image ofobject B 1518B based on the likely position of object B (e.g., thesub-image of object B 1518B may include the distinct spatial area).

Additionally, or alternatively, in the non-limiting example of FIG. 15 ,the object position data of object N 1510N computed by the machinelearning-based object localization model 1508 may define an approximatespatial area (e.g., an approximate bounding box) of object N within thedigital image data of the downsampled digital image 1506. In suchnon-limiting example, process 1420 may function to identify a likelyposition of object N within the digital image data of the originaldigital image 1502. In some embodiments, identifying the likely positionof object N within the digital image data of the original digital image1502 may include correlating, via one or more processors, theapproximate spatial area of object N within the digital image data ofthe downsampled digital image 1506 to a distinct spatial area in digitalimage data of the original digital image 1502. Additionally, in suchnon-limiting example, process 1430 may function to extract, from thedigital image data of the original digital image 1502, a sub-image ofobject N 1518N based on the likely position of object N (e.g., thesub-image of object N 1518N may include the distinct spatial area).

Stated another way, in one or more embodiments, the object extractionmodel 1516 may function to output the sub-image of object A 1518A (e.g.,a sub-image containing object A) based on extracting, from the originaldigital image 1502, the original image data contained in bounding box1812A, as shown generally by way of example in FIG. 18 . Additionally,or alternatively, in such embodiments, the object extraction model 1516may function to output the sub-image of object B 1518B (e.g., asub-image containing object B) based on extracting, from the originaldigital image 1502, the original image data contained in bounding box1812B. Additionally, or alternatively, in such embodiments, the objectextraction model 1516 may function to output the sub-image of object C1518C (e.g., a sub-image containing object C) based on extracting, fromthe original digital image 1502, the original image data contained inbounding box 1812C. Additionally, or alternatively, in such embodiments,the object extraction model 1516 may function to output the sub-image ofobject N 1518N (e.g., a sub-image containing object N) based onextracting, from the original digital image 1502, the original imagedata contained in bounding box 1812N.

Second Stage of the Multi-Stage Object Classification and ConditionPipeline

In one or more embodiments, the method 1400 may include process 1440.Process 1440 may be used within the second stage 1534 of the multi-stageobject classification and condition pipeline 1501. The second stage1534, in one or more embodiments, may receive one or more sub-images ofthe original digital image 1502 and classify each of the one or moresub-images to a probable object class, as described in more detailherein. It shall be recognized that the “second stage” may beinterchangeably referred to herein as a “second modeling stage”, an“object classification stage”, or the like.

In one or more embodiments, each of the one or more sub-images outputtedby the object extraction model 1516 may be provided to a sub-imageresizing model 1520. The sub-image resizing model, in one or moreembodiments, may be configured to transform each of the one or moresub-images to an image resolution that satisfies an input imageresolution criterion (e.g., a target image resolution parameter or thelike) of the machine learning-based object classification model 1524and/or the object-condition machine learning classification model 1528,as shown generally by way of example in FIG. 15 and FIG. 19 .

For instance, in the non-limiting example of FIG. 15 , the sub-image ofobject A 1518A may be provided, as input, to the sub-image resizingmodel 1520. Accordingly, based on the sub-image resizing model 1520receiving the sub-image of object A 1518A, the sub-image resizing model1520 may output a transformed sub-image of object A 1522A that may havean image resolution that satisfies the input image resolution criterionof the machine learning-based object classification model 1524.Additionally, or alternatively, in such non-limiting example, based onthe sub-image resizing model 1520 receiving the sub-image of object B1518B, the sub-image resizing model 1520 may output a transformedsub-image of object B 1522B that may have an image resolution thatsatisfies the input image resolution criterion of the machinelearning-based object classification model 1524. Additionally, oralternatively, in such non-limiting example, based on the sub-imageresizing model 1520 receiving the sub-image of object N 1518N, thesub-image resizing model 1520 may output a transformed sub-image ofobject N 1522N that may have an image resolution that satisfies theinput image resolution criterion of the machine learning-based objectclassification model 1524. It shall be noted that a “transformedsub-image” may also be interchangeably referred to herein as a “resizedsub-image” or the like.

In one or more embodiments, each of the transformed sub-images may beprovided, as input, to the machine learning-based object classificationmodel 1524. In such embodiments, based on the machine learning-basedobject classification model 1524 receiving a transformed sub-image,process 1440 may be commenced. Process 1440 may include classifying, viathe machine learning-based object classification model 1524, eachtransformed sub-image to a probable object class of a plurality ofdistinct object classes. Stated differently, process 1440 may classify,via the machine learning-based object classification model 1524, anobject contained within digital image data of a subject transformedsub-image to a probable object class of a plurality of distinct objectclasses.

In one or more embodiments, the machine learning-based objectclassification model 1524 may be configured to classify a subjecttransformed sub-image to an in-scope object class of one or morein-scope object classes or an out-of-scope object class (e.g., a notapplicable object class or the like) based on a set of featuresextracted from the subject transformed sub-image. That is, in someembodiments, the machine learning-based object classification model 1524may classify the subject transformed sub-image to one of the one or morein-scope object classes. Alternatively, in some embodiments, the machinelearning-based object classification model 1524 may classify the subjecttransformed sub-image to the out-of-scope object class.

It shall be recognized that, in some embodiments, the subjecttransformed sub-image may be germane to a target classificationobjective of a downstream object-condition machine learningclassification model (e.g., the object-condition machine learningclassification model 1528) when the machine learning-based objectclassification model 1524 classifies the subject sub-image to one of theone or more in-scope object classes. It shall be further recognizedthat, in some embodiments, the transformed subject sub-image may beimmaterial to a target classification objective of a downstreamobject-condition machine learning classification model (e.g., theobject-condition machine learning classification model 1528) when themachine learning-based object classification model 1524 classifies thesubject transformed sub-image to the out-of-scope object class.

For instance, in a non-limiting example, the machine learning-basedobject classification model 1524 may function to classify thetransformed sub-image of object A 1522A to a probable object class(e.g., probable object class of object A 1526A). In such embodiments,the probable object class of object A 1526A may relate to an in-scopeobject class (e.g., electrical insulator class). Thus, the machinelearning-based object localization model 1508 may have correctlylocalized object A within the digital image data of the downsampleddigital image 1506 as the object (e.g., object A, electrical insulatorA, etc.) contained within the transformed sub-image of object A 1522Alikely corresponds to a target object class (e.g., electrical insulatorclass) that the machine learning-based object localization model 1508was trained to identify.

Additionally, or alternatively, in such non-limiting example, themachine learning-based object classification model 1524 may function toclassify the transformed sub-image of object B 1522B to a probableobject class (e.g., probable object class of object B 1526B). In suchembodiments, the probable object class of object B 1526B may relate toan in-scope object class (e.g., electrical insulator class). Thus, themachine learning-based object localization model 1508 may have correctlylocalized object B within the digital image data of the downsampleddigital image 1506 as the object (e.g., object B, electrical insulatorB) contained within the transformed sub-image of object B 1522B likelycorresponds to a target object class (e.g., electrical insulator class)that the machine learning-based object localization model 1508 wastrained to identify.

Additionally, or alternatively, in such non-limiting example, themachine learning-based object classification model 1524 may function toclassify the transformed sub-image of object N 1522N to a probableobject class (e.g., probable object class of object N 1526N). In suchembodiments, the probable object class of object N 1526N may relate toan in-scope object class (e.g., electrical insulator class). Thus, themachine learning-based object localization model 1508 may have correctlylocalized object N within the digital image data of the downsampleddigital image 1506 as the object (e.g., object N, electrical insulatorN) contained within the transformed sub-image of object N 1522N likelycorresponds to a target object class (e.g., electrical insulator class)that the machine learning-based object localization model 1508 wastrained to identify.

It shall be recognized that, in some embodiments, the machinelearning-based object localization model 1508 may have incorrectlylocalized an object within digital image data of the downsampled digitalimage 1506. For instance, in the non-limiting example of FIG. 19 , themachine learning-based object classification model 1524 may haveclassified the transformed sub-image of object C 1522C to anout-of-scope class (e.g., not applicable class). Thus, the object (e.g.,object C, a water puddle, etc.) contained within the transformedsub-image of object C 1522C likely does not correspond to a targetobject class that the machine learning-based object localization model1508 was trained to identify.

At least one technical benefit of using the machine learning-basedobject classification model 1524 to classify each of the transformedsub-images may enable a rectification of localization error (e.g.,correct inaccuracies) caused by the machine learning-based objectlocalization model 1508 of the first stage 1532 of the multi-stageobject classification and condition pipeline 1501. Furthermore, anothertechnical advantage of using the machine learning-based objectclassification model 1524 may result in a higher (e.g., increased)classification accuracy when compared to a classification accuracy of acombined localization and classification machine learning model as thecombined localization and classification machine learning model isattempting to balance accuracy across a series of predictions (e.g.,classification and localization) rather than specialize to a singlemachine learning task type (e.g., object classification).

Third Stage of the Multi-Stage Object Classification and ConditionPipeline

In one or more embodiments, the method 1400 may include process 1450and/or process 1460. Process 1450 and process 1460 may be used withinthe third stage 1536 of the multi-stage object classification andcondition pipeline 1501. It shall be recognized that the “third stage”may be interchangeably referred to herein as a “third modeling stage”,an “object-condition classification stage”, or the like.

In one or more embodiments, based on the machine learning-based objectclassification model 1524 classifying a subject transformed sub-image toan in-scope object class, process 1450 may be commenced. Process 1450,in one or more embodiments, may include routing, via one or moreprocessors, at least a subset of transformed sub-images to one or moretarget object-condition machine learning classification models in whicheach transformed sub-image of the subset satisfies routing criteria ofthe multi-stage object classification and condition pipeline 1501.

In one or more embodiments, a subject transformed sub-image may satisfyrouting criteria when the machine learning-based object classificationmodel 1524 classifies the subject transformed sub-image to one of theone or more in-scope object classes. It shall be recognized, in one ormore embodiments, a subject transformed sub-image may not satisfyrouting criteria when the machine learning-based object classificationmodel 1524 classifies the subject transformed sub-image to theout-of-scope object class (e.g., not applicable object class).

In one or more embodiments, based on a subject transformed sub-imagesatisfying routing criteria of the multi-stage object classification andcondition pipeline 1501, the subject transformed sub-image may be routedto a target object-condition machine learning model of one or moretarget object-condition machine learning models based on the in-scopeobject class associated with the subject transformed sub-image.Accordingly, in some embodiments, process 1450 may function to assess amapping that includes one or more distinct routing nexuses in which eachdistinct routing nexus associates a distinct object class (e.g.,distinct in-scope object class) to a distinct object-condition machinelearning model. Stated another way, in one or more embodiments, eachdistinct routing nexus defines a routing relationship between a distinctobject class and a distinct object-condition machine learning model,thereby defining a path for routing a subject transformed sub-image tothe appropriate (e.g., specialized) object-condition machine learningclassification model based on the classification of the subjecttransformed sub-image by the machine learning-based objectclassification model 1524. It shall be recognized that the mappingincluding the one or more distinct routing nexuses may provide atechnical benefit of enabling the system or service implementing themethod 1400 to route transformed sub-images to one or more appropriateobject-condition machine learning classification models.

For instance, in one or more embodiments, the mapping may include afirst routing nexus that associates a first object class (e.g., anelectrical insulator object class) to an insulator object-conditionmachine learning model 1920. Additionally, or alternatively, the mappingmay include a second routing nexus that associates a second object class(e.g., electrical transformer object class) to a transformerobject-condition machine learning model 1918. It shall be noted that themapping may include additional routing nexuses, fewer routing nexuses,and/or different routing nexuses without departing from the scope of thedisclosure.

In a non-limiting example, the transformed sub-image of object A 1522Amay be provided to the machine learning-based object classificationmodel 1524 which, in turn, may classify the transformed sub-image ofobject A 1522A to an electrical insulator class (e.g., in-scope objectclass). Further, in such non-limiting example, process 1450 may functionto identify a routing nexus of the mapping that correspond to theelectrical insulator class. Accordingly, based on identifying therouting nexus that corresponds to the electrical insulator class,process 1450 may function to route the transformed sub-image of object A1522A to the insulator object-condition model 1920 as the routing nexusdefines routing a subject transformed sub-image to the insulatorobject-condition model 1920 when the machine learning-based objectclassification model 1524 classifies the subject transformed sub-imageto the electrical insulator class.

Additionally, or alternatively, in a non-limiting example, thetransformed sub-image of object B 1522B may be provided to the machinelearning-based object classification model 1524 which, in turn, mayclassify the transformed sub-image of object B 1522B to an electricalinsulator class (e.g., in-scope object class). Further, in suchnon-limiting example, process 1450 may function to identify a routingnexus of the mapping that correspond to the electrical insulator class.Accordingly, based on identifying the routing nexus that corresponds tothe electrical insulator class, process 1450 may function to route thetransformed sub-image of object B 1522B to the insulatorobject-condition model 1920 as the routing nexus defines routing asubject transformed sub-image to the insulator object-condition model1920 when the machine learning-based object classification model 1524classifies the subject transformed sub-image to the electrical insulatorclass.

Additionally, or alternatively, in a non-limiting example, thetransformed sub-image of object C 1522C may be provided to the machinelearning-based object classification model 1524 which, in turn, mayclassify the transformed sub-image of object C 1522C to an out-of-scopeobject class (e.g., not applicable class, probable object class ofobject C 1526C). In such non-limiting example, process 1450 may functionto bypass an object-condition classification of the transformedsub-image of object C 1522C when the machine learning-based objectclassification model 1524 classifies a subject transformed sub-image tothe out-of-scope object class (e.g., not applicable object class).Stated another way, process 1450 may forego a routing of the transformedsub-image of object C 1522C to an object-condition classification modelbased on the machine learning-based object classification model 1524classifying the transformed sub-image of object C 1522C to theout-of-scope object class.

Additionally, or alternatively, in a non-limiting example, thetransformed sub-image of object N 1522N may be provided to the machinelearning-based object classification model 1524 which, in turn, mayclassify the transformed sub-image of object N 1522N to an electricalinsulator class (e.g., in-scope object class). Further, in suchnon-limiting example, process 1450 may function to identify a routingnexus of the mapping that correspond to the electrical insulator class.Accordingly, based on identifying the routing nexus that corresponds tothe electrical insulator class, process 1450 may function to route thetransformed sub-image of object N 1522N to the insulatorobject-condition model 1920 as the routing nexus defines routing asubject transformed sub-image to the insulator object-condition model1920 when the machine learning-based object classification model 1524classifies the subject transformed sub-image to the electrical insulatorclass.

In one or more embodiments, based on routing a subject transformedsub-image to a target object-condition machine learning classificationmodel, process 1460 may be commenced. Process 1460, in one or moreembodiments, may function to classify, via a target object-conditionmachine learning classification model, a subject transformed sub-imageto a probable object-condition class of a plurality of distinctobject-condition classes based on a routing of the subject transformedsub-image to the target object-condition machine learning classificationmodel.

For instance, in a non-limiting example, based on routing thetransformed sub-image of object A 1522A to the insulatorobject-condition model 1920 (e.g., insulator object-condition machinelearning model), the insulator object-condition model 1920 may classifythe transformed sub-image of object A 1522A to a probableobject-condition class (e.g., not defective object class). Statedanother way, the object (e.g., object A) contained within thetransformed sub-image of object A 1522A is not defective (e.g., theelectrical insulator does not have a broken shell, the electricalinsulator does not have shock damage, etc.).

Additionally, or alternatively, in a non-limiting example, based onrouting the transformed sub-image of object B 1522B to the insulatorobject-condition model 1920 (e.g., insulator object-condition machinelearning model), the insulator object-condition model 1920 may classifythe transformed sub-image of object B 1522B to a probableobject-condition class (e.g., not defective object class). Statedanother way, the object (e.g., object B) contained within thetransformed sub-image of object B 1522B is not defective (e.g., theelectrical insulator does not have a broken shell, the electricalinsulator does not have shock damage, etc.).

Additionally, or alternatively, in a non-limiting example, based onrouting the transformed sub-image of object N 1522N to the insulatorobject-condition model 1920 (e.g., insulator object-condition machinelearning model), the insulator object-condition model 1920 may classifythe transformed sub-image of object N 1522N to a probableobject-condition class (e.g., defective object class). Stated anotherway, the object (e.g., object N) contained within the transformedsub-image of object N 1522N is defective (e.g., the electrical insulatorhas one or more broken shells, the electrical insulator has shockdamage, and/or the like.).

It shall be recognized that, in embodiments in which a transformedsub-image contains an object associated with an electrical transformer,process 1450 may function to route the transformed sub-image to thetransformer object-condition machine learning model 1918 in analogousways. In such embodiments, process 1460 may classify, via thetransformer object-condition machine learning model 1918, thetransformed sub-image to an object condition-class of a plurality ofdistinct object-condition classes associated with electricaltransformers.

In another non-limiting example, the machine learning-based objectclassification model 1524 may function to classify the transformedsub-image of object I 1602A, the transformed sub-image of object II1602B, and the transformed sub-image of object III 1604C to a firstin-scope object class (e.g., probable object class of object I), anout-of-scope object class (e.g., probable object class of object II),and a second in-scope object class (e.g., probable object class ofobject III), respectively.

In such non-limiting example, process 1450 may function to route thetransformed sub-image of object I 1602A to an object-condition machinelearning model (e.g., object-condition machine learning classificationmodel A 16106A) of the plurality of object-condition machine learningclassification models 1606 that specializes in detecting objectconditions of objects of the first in-scope object class, as showngenerally by way of example in FIG. 16 . Accordingly, based on therouting, the object-condition machine learning classification model A16106A may classify the transformed sub-image of object I 1602A to aprobable object-condition class. Stated another way, object I containedwithin the transformed sub-image of object I 1602A likely corresponds tothe probable object-condition class predicted by the object-conditionmachine learning classification model A 16106A.

Additionally, or alternatively, in such non-limiting example, based onthe machine learning-based object classification model classifying thetransformed sub-image of object II 1602B to the out-of-scope class,process 1450 may function to forego routing the transformed sub-image ofobject II 1602B to one of the plurality of object-condition machinelearning classification models 1606 based on routing criteria of themulti-stage object classification and condition pipeline 1501 not beingsatisfied, as described above.

Additionally, or alternatively, in such non-limiting example, process1450 may function to route the transformed sub-image of object III 1602Cto an object-condition machine learning model (e.g., object-conditionmachine learning classification model A 16106B) of the plurality ofobject-condition machine learning classification models 1606 thatspecializes in detecting object conditions of objects of the secondin-scope object class, as shown generally by way of example in FIG. 16 .Accordingly, based on the routing, the object-condition machine learningclassification model B 16106B may classify the transformed sub-image ofobject III 1602C to a probable object-condition class. Stated anotherway, object III contained within the transformed sub-image of object III1602C likely corresponds to the probable object-condition classpredicted by the object-condition machine learning classification modelB 16106B.

In one or more embodiments, based on one or more object-conditionmachine learning classification models classifying a set of transformedsub-images to a probable object-condition class, process 1470 may becommenced. Process 1470, in one or more embodiments, may includedisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition class ofthe target object.

In one or more embodiments, the graphical user interface may display arepresentation of a subject object in association with the probableobject condition-class for each transformed sub-image classified by oneor more object-condition machine learning classification models. In suchembodiments, the graphical user interface may display a first set ofsub-images (sub-image of object A 1518A, sub-image of object B 1518B) inassociated with the probable object-condition class that corresponds tothe not defective object-condition class. Additionally, in suchembodiments, the graphical user interface may display a set second ofsub-images (e.g., sub-image of object N 1518N) in associated with theprobable object-condition class that corresponds to the defectiveobject-condition class.

In a variant to such embodiments, the graphical user interface maydisplay one or more representations of one or more objects thatcorrespond to a target object-condition class (e.g., defective objectcondition-class). In such embodiments, the graphical user interface maydisplay the set of sub-images (e.g., sub-image of object N 1518N thatcorrespond to the defective object condition-class. The graphical userinterface, in some embodiments, may allow users to visually understandthe classification results and/or condition of subject objectsclassified using the multi-stage object classification and conditionpipeline 1501.

It shall also be noted that the system and methods of the embodiment andvariations described herein can be embodied and/or implemented at leastin part as a machine configured to receive a computer-readable mediumstoring computer-readable instructions. The instructions are preferablyexecuted by computer-executable components preferably integrated withthe system and one or more portions of the processors and/or thecontrollers. The computer-readable medium can be stored on any suitablecomputer-readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, memory sticks(e.g., SD cards, USB flash drives), cloud-based services (e.g., cloudstorage), magnetic storage devices, Solid-State Drives (SSDs), or anysuitable device. The computer-executable component is preferably ageneral or application-specific processor, but any suitable dedicatedhardware or hardware/firmware combination device can alternatively oradditionally execute the instructions.

The systems and methods of the preferred embodiments may additionally,or alternatively, be implemented on an integrated data analyticssoftware application and/or software architecture such as that areoffered by SAS Institute Inc. of Cary, N.C., USA. Merely forillustration, the systems and methods of the preferred embodiments maybe implemented using or integrated with one or more SAS software toolssuch as SAS® Viya™ which is developed and provided by SAS Institute Inc.of Cary, N.C., USA.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the implementations of the systemsand methods described herein.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the disclosure without departing fromthe scope of the various described embodiments.

What is claimed is:
 1. A computer-program product embodied in anon-transitory machine-readable storage medium storing computerinstructions that, when executed by one or more processors, performoperations comprising: detecting, via a localization machine learningmodel, a target object within a scene based on downsampled image data ofthe scene; identifying, via the one or more processors, a likelyposition of the target object within original image data of the scenebased on object position data computed by the localization machinelearning model; extracting, from the original image data of the scene, atarget sub-image containing the target object based on the likelyposition of the target object; classifying, via an object classificationmachine learning model, the target object to a probable object class ofa plurality of distinct object classes based on a target imageresolution of the target sub-image, wherein the plurality of distinctobject classes includes an out-of-scope object class and one or morein-scope object classes; routing, via the one or more processors, thetarget image resolution of the target sub-image to a targetobject-condition machine learning classification model of a plurality ofdistinct object-condition machine learning classification models basedon a mapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels; classifying, via the target object-condition machine learningclassification model, the target object to a probable object-conditionclass of a plurality of distinct object-condition classes; anddisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition classbased on the classifying of the target object via the targetobject-condition machine learning classification model.
 2. Thecomputer-program product according to claim 1, further comprising:generating the localization machine learning model, wherein generatingthe localization machine learning model includes: implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.
 3. The computer-program product according toclaim 2, wherein: the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.
 4. The computer-program productaccording to claim 1, wherein: the object position data computed by thelocalization machine learning model defines an approximate spatial areaof the target object in the downsampled image data of the scene;identifying the likely position of the target object within the originalimage data of the scene includes: correlating, via the one or moreprocessors, the approximate spatial area of the target object to adistinct spatial area in the original image data of the scene; and thetarget sub-image extracted from the original image data of the sceneincludes the distinct spatial area.
 5. The computer-program productaccording to claim 1, wherein: the scene further includes a secondobject distinct from the target object; the computer-program productfurther includes computer instructions for performing operationsincluding: identifying, via the one or more processors, a likelyposition of the second object within the original image data of thescene based on the object position data computed by the localizationmachine learning model; and extracting, from the original image data ofthe scene, a second sub-image containing the second object based on thelikely position of the second object.
 6. The computer-program productaccording to claim 5, further comprising: classifying, via the objectclassification machine learning model, the second object to a probableobject class of the plurality of distinct object classes based on atarget image resolution of the second sub-image, wherein: the objectclassification machine learning model classifies the second object tothe out-of-scope object class and the target object to one of the one ormore in-scope object classes; the out-of-scope object class indicatesthat the second sub-image is immaterial to a target classificationobjective of the plurality of distinct object-condition machine learningclassification models; and the one of the one or more in-scope objectclasses indicates that the target sub-image is germane to the targetclassification objective of the plurality of distinct object-conditionmachine learning classification models.
 7. The computer-program productaccording to claim 6, further comprising: bypassing an object-conditionclassification of the second sub-image when the object classificationmachine learning model classifies the second object to the out-of-scopeobject class.
 8. The computer-program product according to claim 5,further comprising: classifying, via the object classification machinelearning model, the second object to a probable object class of theplurality of distinct object classes based on a target image resolutionof the second sub-image, wherein: the one or more in-scope objectclasses include a first in-scope object class and a second in-scopeobject class; the object classification machine learning modelclassifies the target object to the first in-scope object class; theobject classification machine learning model classifies the secondobject to the second in-scope object class; and wherein the routingfurther includes: routing the target image resolution of the targetsub-image to a distinct object-condition machine learning classificationmodel of the plurality of distinct object-condition machine learningclassification models that corresponds to the first in-scope objectclass; and routing the target image resolution of the second sub-imageto a distinct object-condition machine learning classification model ofthe plurality of distinct object-condition machine learningclassification models that corresponds to the second in-scope objectclass.
 9. The computer-program product according to claim 1, wherein:the mapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels defines a plurality of distinct routing nexuses; each distinctrouting nexus of the plurality of distinct routing nexuses associates adistinct one of the plurality of distinct object classes to a distinctone of the plurality of distinct object-condition machine learningclassification models; and the routing of the target image resolution ofthe target sub-image is further based on identifying a target routingnexus that corresponds to the probable object class of the targetobject.
 10. The computer-program product according to claim 1, wherein:the probable object class indicates a probable object type of the targetobject; and the probable object-condition class indicates a probableoperating state of the target object.
 11. The computer-program productaccording to claim 1, further comprising: implementing a multi-stageobject classification and condition pipeline, wherein: a first stage ofthe multi-stage object classification and condition pipeline includesthe detecting, by the localization machine learning model, of the targetobject within the scene; a second stage of the multi-stage objectclassification and condition pipeline includes the classifying, by theobject classification machine learning model, of the target object tothe probable object class; and a third stage of the multi-stage objectclassification and condition pipeline includes the classifying, by thetarget object-condition machine learning classification model, of thetarget object to the probable object-condition class.
 12. Thecomputer-program product according to claim 1, wherein: the originalimage data of the scene has a first image resolution; and thedownsampled image data of the scene has a second image resolution lowerthan the first image resolution.
 13. The computer-program productaccording to claim 1, wherein: the downsampled image data of the scenesatisfies a target image resolution parameter of the localizationmachine learning model.
 14. A computer-implemented method comprising:detecting, via a localization machine learning model, a target objectwithin a scene based on downsampled image data of the scene;identifying, via the one or more processors, a likely position of thetarget object within original image data of the scene based on objectposition data computed by the localization machine learning model;extracting, from the original image data of the scene, a targetsub-image containing the target object based on the likely position ofthe target object; classifying, via an object classification machinelearning model, the target object to a probable object class of aplurality of distinct object classes based on a target image resolutionof the target sub-image, wherein the plurality of distinct objectclasses includes an out-of-scope object class and one or more in-scopeobject classes; routing, via the one or more processors, the targetimage resolution of the target sub-image to a target object-conditionmachine learning classification model of a plurality of distinctobject-condition machine learning classification models based on amapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels; classifying, via the target object-condition machine learningclassification model, the target object to a probable object-conditionclass of a plurality of distinct object-condition classes; anddisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition classbased on the classifying of the target object via the targetobject-condition machine learning classification model.
 15. Thecomputer-implemented method according to claim 14, further comprising:generating the localization machine learning model, wherein generatingthe localization machine learning model includes: implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.
 16. The computer-implemented method according toclaim 15, wherein: the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.
 17. The computer-implementedmethod according to claim 14, wherein: the object position data computedby the localization machine learning model defines an approximatespatial area of the target object in the downsampled image data of thescene; identifying the likely position of the target object within theoriginal image data of the scene includes: correlating, via the one ormore processors, the approximate spatial area of the target object to adistinct spatial area in the original image data of the scene; and thetarget sub-image extracted from the original image data of the sceneincludes the distinct spatial area.
 18. The computer-implemented methodaccording to claim 14, wherein: the scene further includes a secondobject distinct from the target object; the computer-program productfurther includes computer instructions for performing operationsincluding: identifying, via the one or more processors, a likelyposition of the second object within the original image data of thescene based on the object position data computed by the localizationmachine learning model; and extracting, from the original image data ofthe scene, a second sub-image containing the second object based on thelikely position of the second object.
 19. The computer-implementedmethod according to claim 18, further comprising: classifying, via theobject classification machine learning model, the second object to aprobable object class of the plurality of distinct object classes basedon a target image resolution of the second sub-image, wherein: theobject classification machine learning model classifies the secondobject to the out-of-scope object class and the target object to one ofthe one or more in-scope object classes; the out-of-scope object classindicates that the second sub-image is immaterial to a targetclassification objective of the plurality of distinct object-conditionmachine learning classification models; and the one of the one or morein-scope object classes indicates that the target sub-image is germaneto the target classification objective of the plurality of distinctobject-condition machine learning classification models.
 20. Thecomputer-implemented method according to claim 19, further comprising:bypassing an object-condition classification of the second sub-imagewhen the object classification machine learning model classifies thesecond object to the out-of-scope object class.
 21. Thecomputer-implemented method according to claim 18, further comprising:classifying, via the object classification machine learning model, thesecond object to a probable object class of the plurality of distinctobject classes based on a target image resolution of the secondsub-image, wherein: the one or more in-scope object classes include afirst in-scope object class and a second in-scope object class; theobject classification machine learning model classifies the targetobject to the first in-scope object class; the object classificationmachine learning model classifies the second object to the secondin-scope object class; and wherein the routing further includes: routingthe target image resolution of the target sub-image to a distinctobject-condition machine learning classification model of the pluralityof distinct object-condition machine learning classification models thatcorresponds to the first in-scope object class; and routing the targetimage resolution of the second sub-image to a distinct object-conditionmachine learning classification model of the plurality of distinctobject-condition machine learning classification models that correspondsto the second in-scope object class.
 22. The computer-implemented methodaccording to claim 14, wherein: the mapping between the plurality ofdistinct object classes and the plurality of distinct object-conditionmachine learning classification models defines a plurality of distinctrouting nexuses; each distinct routing nexus of the plurality ofdistinct routing nexuses associates a distinct one of the plurality ofdistinct object classes to a distinct one of the plurality of distinctobject-condition machine learning classification models; and the routingof the target image resolution of the target sub-image is further basedon identifying a target routing nexus that corresponds to the probableobject class of the target object.
 23. The computer-implemented methodaccording to claim 14, wherein: the probable object class indicates aprobable object type of the target object; and the probableobject-condition class indicates a probable operating state of thetarget object.
 24. The computer-implemented method according to claim14, further comprising: implementing a multi-stage object classificationand condition pipeline, wherein: a first stage of the multi-stage objectclassification and condition pipeline includes the detecting, by thelocalization machine learning model, of the target object within thescene; a second stage of the multi-stage object classification andcondition pipeline includes the classifying, by the objectclassification machine learning model, of the target object to theprobable object class; and a third stage of the multi-stage objectclassification and condition pipeline includes the classifying, by thetarget object-condition machine learning classification model, of thetarget object to the probable object-condition class.
 25. Thecomputer-implemented method according to claim 14, wherein: the originalimage data of the scene has a first image resolution; and thedownsampled image data of the scene has a second image resolution lowerthan the first image resolution.
 26. The computer-implemented methodaccording to claim 14, wherein: the downsampled image data of the scenesatisfies a target image resolution parameter of the localizationmachine learning model.
 27. A computer-implemented system comprising:one or more processors; a memory; a computer-readable medium operablycoupled to the one or more processors, the computer-readable mediumhaving computer-readable instructions stored thereon that, when executedby the one or more processors, cause a computing device to performoperations comprising: detecting, via a localization machine learningmodel, a target object within a scene based on downsampled image data ofthe scene; identifying, via the one or more processors, a likelyposition of the target object within original image data of the scenebased on object position data computed by the localization machinelearning model; extracting, from the original image data of the scene, atarget sub-image containing the target object based on the likelyposition of the target object; classifying, via an object classificationmachine learning model, the target object to a probable object class ofa plurality of distinct object classes based on a target imageresolution of the target sub-image, wherein the plurality of distinctobject classes includes an out-of-scope object class and one or morein-scope object classes; routing, via the one or more processors, thetarget image resolution of the target sub-image to a targetobject-condition machine learning classification model of a plurality ofdistinct object-condition machine learning classification models basedon a mapping between the plurality of distinct object classes and theplurality of distinct object-condition machine learning classificationmodels; classifying, via the target object-condition machine learningclassification model, the target object to a probable object-conditionclass of a plurality of distinct object-condition classes; anddisplaying, via a graphical user interface, a representation of thetarget object in association with the probable object-condition classbased on the classifying of the target object via the targetobject-condition machine learning classification model.
 28. Thecomputer-implemented system according to claim 27, further comprising:generating the localization machine learning model, wherein generatingthe localization machine learning model includes: implementing an objectdetection machine learning model; adapting a multi-criteria lossfunction of the object detection machine learning model to a targetmulti-criteria loss function based on eliminating one or more lossfunction components of the multi-criteria loss function; andtransforming the object detection machine learning model to thelocalization machine learning model based on implementing the targetmulti-criteria loss function during a training of the object detectionmachine learning model.
 29. The computer-implemented system according toclaim 28, wherein: the multi-criteria loss function of the objectdetection machine learning model includes an object classification errorcomponent; and the target multi-criteria loss function excludes theobject classification error component.
 30. The computer-implementedsystem according to claim 27, wherein: the object position data computedby the localization machine learning model defines an approximatespatial area of the target object in the downsampled image data of thescene; identifying the likely position of the target object within theoriginal image data of the scene includes: correlating, via the one ormore processors, the approximate spatial area of the target object to adistinct spatial area in the original image data of the scene; and thetarget sub-image extracted from the original image data of the sceneincludes the distinct spatial area.